CN109695579B - Early warning method and device for wind driven generator - Google Patents
Early warning method and device for wind driven generator Download PDFInfo
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- CN109695579B CN109695579B CN201811629574.XA CN201811629574A CN109695579B CN 109695579 B CN109695579 B CN 109695579B CN 201811629574 A CN201811629574 A CN 201811629574A CN 109695579 B CN109695579 B CN 109695579B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D17/00—Radial-flow pumps, e.g. centrifugal pumps; Helico-centrifugal pumps
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2260/00—Function
- F05B2260/80—Diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2260/00—Function
- F05B2260/84—Modelling or simulation
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- General Engineering & Computer Science (AREA)
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Abstract
The invention provides an early warning method and an early warning device for a wind driven generator, which comprise the following steps: collecting an audio time domain signal; comparing the decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain first result information; carrying out Fourier transform on the audio time domain signal to obtain frequency domain characteristic data; inputting the frequency domain characteristic data into a Gaussian mixture model to obtain second result information; and alarming is carried out according to the first result information or the second result information, so that the mechanical fault of the wind driven generator can be detected in time, and the detection accuracy is improved.
Description
Technical Field
The invention relates to the technical field of wind driven generators, in particular to an early warning method and device of a wind driven generator.
Background
The mechanical fault of the wind driven generator often causes major accidents, the existing monitoring on the mechanical fault is to collect audio signals of all parts through an audio sensor and compare the frequency spectrums of the audio signals, so that the fault is judged, the detection mode can cause untimely detection, and the accuracy is low.
Disclosure of Invention
In view of this, the present invention provides an early warning method and an early warning device for a wind turbine generator, which can detect mechanical faults of the wind turbine generator in time and improve the accuracy of detection.
In a first aspect, an embodiment of the present invention provides an early warning method for a wind turbine, where the method includes:
collecting an audio time domain signal;
comparing the decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain first result information;
performing Fourier transform on the audio time domain signal to obtain frequency domain characteristic data;
inputting the frequency domain characteristic data into a Gaussian mixture model to obtain second result information;
and alarming according to the first result information or the second result information.
Further, the performing fourier transform on the audio time domain signal to obtain frequency domain characteristic data includes:
framing the audio time domain signal to obtain a multi-frame audio time domain signal;
and respectively carrying out Fourier transform on each frame of audio time domain signal to obtain the frequency domain characteristic data.
Further, the second result information includes abnormal result information, and the inputting the frequency domain feature data into the gaussian mixture model to obtain the second result information includes:
comparing the frequency domain characteristic data with frequency domain characteristic data in an abnormal database, and determining the similarity between the frequency domain characteristic data and the frequency domain characteristic data in the abnormal database;
and if the similarity reaches a matching threshold, generating the abnormal result information.
Further, the comparing the decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain first result information includes:
judging whether a decibel value corresponding to the audio time domain signal is greater than the decibel threshold value or not;
and if the decibel value corresponding to the audio time domain signal is greater than the decibel threshold value, generating alarm information.
Further, the determining whether a decibel value corresponding to the audio time domain signal is greater than the decibel threshold includes:
and if the decibel value corresponding to the audio time domain signal is smaller than the decibel threshold value, generating normal result information.
In a second aspect, an embodiment of the present invention provides an early warning apparatus for a wind turbine, where the apparatus includes:
the acquisition unit is used for acquiring audio time domain signals;
the comparison unit is used for comparing the decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain first result information;
the frequency domain characteristic data acquisition unit is used for carrying out Fourier transform on the audio time domain signal to obtain frequency domain characteristic data;
the second result information acquisition unit is used for inputting the frequency domain characteristic data into a Gaussian mixture model to obtain second result information;
and the alarm unit is used for giving an alarm according to the first result information or the second result information.
Further, the frequency domain characteristic data obtaining unit includes:
framing the audio time domain signal to obtain a multi-frame audio time domain signal;
and respectively carrying out Fourier transform on each frame of audio time domain signal to obtain the frequency domain characteristic data.
Further, the second result information acquiring unit includes:
comparing the frequency domain characteristic data with frequency domain characteristic data in an abnormal database, and determining the similarity between the frequency domain characteristic data and the frequency domain characteristic data in the abnormal database;
and if the similarity reaches a matching threshold, generating the abnormal result information.
Further, the comparison unit includes:
judging whether a decibel value corresponding to the audio time domain signal is greater than the decibel threshold value or not;
and if the decibel value corresponding to the audio time domain signal is greater than the decibel threshold value, generating alarm information.
Further, the comparison unit includes:
and if the decibel value corresponding to the audio time domain signal is smaller than the decibel threshold value, generating normal result information.
The embodiment of the invention provides an early warning method and an early warning device for a wind driven generator, which comprise the following steps: collecting an audio time domain signal; comparing the decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain first result information; carrying out Fourier transform on the audio time domain signal to obtain frequency domain characteristic data; inputting the frequency domain characteristic data into a Gaussian mixture model to obtain second result information; and alarming is carried out according to the first result information or the second result information, so that the mechanical fault of the wind driven generator can be detected in time, and the detection accuracy is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an early warning method for a wind turbine according to an embodiment of the present invention;
FIG. 2 is a schematic view of operational data of a wind turbine with small wind speed segments according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of audio time-domain signals and frequency-domain signals of a small wind speed segment according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating operational data of a wind turbine generator in a medium wind speed range according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a time-domain signal and a frequency-domain signal of a medium velocity band audio according to an embodiment of the present invention;
FIG. 6 is a schematic view of operational data of a wind turbine generator in a high wind speed range according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a time-domain signal and a frequency-domain signal of an audio frequency in a high wind speed section according to an embodiment of the present invention;
fig. 8 is a schematic view of an early warning device of a wind turbine according to a second embodiment of the present invention;
fig. 9 is a schematic view of an early warning system of a wind turbine according to a third embodiment of the present invention;
fig. 10 is a schematic diagram of the abnormal result information provided by the third embodiment of the present invention.
Icon:
10-an acquisition unit; 20-a comparison unit; 30-a frequency domain characteristic data acquisition unit; 40-a second result information obtaining unit; 50-alarm unit.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The mechanical fault of the wind driven generator often causes major accidents, the existing monitoring on the mechanical fault is to collect audio signals of all parts through an audio sensor and compare the frequency spectrums of the audio signals, so that the fault is judged, the detection mode can cause untimely detection, and the accuracy is low.
According to the method and the device, the audio time domain signals of all the components in the wind driven generator are monitored, working parameters such as the vibration value, the current and the blade angle of the wind driven generator are combined, the running state of the generator is comprehensively judged, and the fault state is alarmed, so that hidden dangers are found in time, the alarm accuracy is improved, and the safe running of the wind driven generator is guaranteed.
For the understanding of the present embodiment, the following detailed description will be given of the embodiment of the present invention.
The first embodiment is as follows:
fig. 1 is a flowchart of an early warning method for a wind turbine according to an embodiment of the present invention.
Referring to fig. 1, the method includes the steps of:
step S101, collecting audio time domain signals;
here, the component in the wind turbine is to provide a sound pickup, and an audio time domain signal is collected through the sound pickup.
Step S102, comparing a decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain first result information;
step S103, carrying out Fourier transform on the audio time domain signal to obtain frequency domain characteristic data;
step S104, inputting the frequency domain characteristic data into a Gaussian mixture model to obtain second result information;
and step S105, alarming according to the first result information or the second result information.
In this embodiment, by acquiring the audio time domain signal, on one hand, a decibel value corresponding to the audio time domain signal is compared with a decibel threshold, on the other hand, fourier transform is performed on the audio time domain signal to obtain frequency domain characteristic data, the frequency domain characteristic data is used as an input of a gaussian mixture model, finally, second result information is output, and an alarm is given according to the first result information or the second result information.
Further, step S103 includes the steps of:
step S201, framing the audio time domain signal to obtain a multi-frame audio time domain signal;
step S202, Fourier transform is respectively carried out on each frame of audio time domain signal to obtain frequency domain characteristic data.
Further, the second result information includes abnormal result information, and the step S104 includes the steps of:
step S301, comparing the frequency domain characteristic data with the frequency domain characteristic data in the abnormal database, and determining the similarity between the frequency domain characteristic data and the frequency domain characteristic data in the abnormal database;
step S302, if the similarity reaches the matching threshold, abnormal result information is generated.
Specifically, after the audio time domain signal is subjected to framing, a plurality of frames of audio time domain signals are obtained, and Fourier transform is performed on each frame of time domain signals to obtain frequency domain characteristic data; and taking the frequency domain characteristic data as the input of the Gaussian mixture model, thereby outputting second result information. And the Gaussian mixture model calls an abnormal database, compares the frequency domain characteristic data with the frequency domain characteristic data in the abnormal database, generates abnormal result information if the similarity between the frequency domain characteristic data and the frequency domain characteristic data in the abnormal database reaches a matching threshold value, and stores the abnormal result information. Wherein, the matching threshold may be 80%, but is not limited to 80%.
Further, step S102 includes the steps of:
step S401, judging whether a decibel value corresponding to the audio time domain signal is greater than a decibel threshold value; if the decibel value corresponding to the audio time domain signal is greater than the decibel threshold, executing step S402; if the decibel value corresponding to the audio time domain signal is less than the decibel threshold, executing step S403;
step S402, generating alarm information;
in step S403, normal result information is generated.
Specifically, when the fault information of the wind driven generator is judged, a decibel value corresponding to the audio time domain signal can be compared with a decibel threshold value, and if the decibel value corresponding to the audio time domain signal is greater than the decibel threshold value, alarm information is generated; and if the decibel value corresponding to the audio time domain signal is smaller than the decibel threshold value, generating normal result information, and indicating that the wind driven generator normally operates.
Fig. 2 is a schematic view of operational data of a wind turbine with a small wind speed range according to an embodiment of the present invention.
Referring to FIG. 2, in the field operation data, the wind speed of a small wind section is 3-5m/s, the average wind speed is 4m/s, the average power is about 210KW, the blade angle (the average value of the actual position of the blade) is kept at 0 degree, the variable pitch current value is stabilized at 5A, the vibration value (the front-back direction, the left-right direction and the up-down direction) of the fan is between 0 g and 0.01g, and most of the vibration value is kept near 0. When the fan normally operates in a small wind section, the noise level in the cabin is usually near 55dB (which means decibel of an audio time domain signal), and within a decibel threshold value, the audio time domain signal is stable and has no abnormal peak noise. Specifically, referring to fig. 3, in the audio time domain signal, the ordinate is dB, and when the fluctuation of the audio time domain signal is small, the dB value is low; in the frequency domain signal, the ordinate is the frequency, and when the frequency domain signal is superimposed more, the frequency is larger.
Fig. 4 is a schematic view of operation data of a wind turbine at a medium wind speed according to an embodiment of the present invention.
Referring to FIG. 4, the wind speed of the middle wind section is 7-8m/s, the average wind speed is about 7.5m/s, the average power is about 1400KW, the angle of the blade (the average value of the actual position of the blade) is mostly kept at 0 degree, the variable pitch current value is increased when the blade is changed, the vibration value of the fan (the front-back direction, the left-right direction and the up-down direction) is 0-0.01 g, and the majority of the vibration value is distributed near 0.01 g. When the wind driven generator in the medium wind speed section normally operates, the noise level is about 65dB, and no peak noise exists. Specifically, referring to fig. 5, in the audio time domain signal, the ordinate is dB, and when the fluctuation of the audio time domain signal is small, the dB value is low; in the frequency domain signal, the ordinate is the frequency, and when the frequency domain signal is superimposed more, the frequency is larger.
Fig. 6 is a schematic view of operation data of a wind turbine generator in a high wind speed section according to an embodiment of the present invention.
Referring to fig. 6, the wind speed of the middle wind section is more than 10m/s, the average wind speed is about 13.5m/s, the average power is about 1300KW, the average power limit value is about 65% (1300KW), the blade angle (the average value of the actual position of the blade) is kept about 10 degrees due to power limit, the variable pitch current value is about 7.5A, and the vibration value (the front-back, left-right, up-down directions) of the fan is in the range of 0.01 g-0.02 g. When the fan operates normally in a high-wind-speed section, the noise level is near 75dB, and no peak noise exists. Referring to fig. 7 specifically, in the audio time domain signal, the ordinate is decibel dB, and when the fluctuation of the audio time domain signal is large, the decibel value is high; in the frequency domain signal, the ordinate is the frequency, and when the frequency domain signal is superimposed more, the frequency is larger.
The embodiment of the invention provides an early warning method of a wind driven generator, which comprises the following steps: collecting an audio time domain signal; comparing the decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain first result information; carrying out Fourier transform on the audio time domain signal to obtain frequency domain characteristic data; inputting the frequency domain characteristic data into a Gaussian mixture model to obtain second result information; and alarming is carried out according to the first result information or the second result information, so that the mechanical fault of the wind driven generator can be detected in time, and the detection accuracy is improved.
Example two:
fig. 8 is a schematic view of an early warning device of a wind turbine according to a second embodiment of the present invention.
Referring to fig. 8, the apparatus is applied to a central control room, and the collected audio time domain signal is processed by the central control room, and the apparatus includes:
the acquisition unit 10 is used for acquiring audio time domain signals;
the comparing unit 20 is configured to compare a decibel value corresponding to the audio time domain signal with a decibel threshold to obtain first result information;
a frequency domain characteristic data obtaining unit 30, configured to perform fourier transform on the audio time domain signal to obtain frequency domain characteristic data;
a second result information obtaining unit 40, configured to input the frequency domain feature data into a gaussian mixture model to obtain second result information;
and an alarm unit 50, configured to alarm according to the first result information or the second result information.
Further, the frequency domain characteristic data acquiring unit 30 includes:
framing the audio time domain signal to obtain a multi-frame audio time domain signal;
and respectively carrying out Fourier transform on each frame of audio time domain signal to obtain frequency domain characteristic data.
Further, the second result information obtaining unit 40 includes:
comparing the frequency domain characteristic data with frequency domain characteristic data in the abnormal database, and determining the similarity between the frequency domain characteristic data and the frequency domain characteristic data in the abnormal database;
and if the similarity reaches a matching threshold, generating abnormal result information.
Further, the comparison unit 20 includes:
judging whether a decibel value corresponding to the audio time domain signal is greater than a decibel threshold value or not;
and if the decibel value corresponding to the audio time domain signal is greater than the decibel threshold value, generating alarm information.
Further, the comparison unit 20 includes:
and if the decibel value corresponding to the audio time domain signal is smaller than the decibel threshold value, generating normal result information.
The embodiment of the invention provides an early warning device of a wind driven generator, which comprises: collecting an audio time domain signal; comparing the decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain first result information; carrying out Fourier transform on the audio time domain signal to obtain frequency domain characteristic data; inputting the frequency domain characteristic data into a Gaussian mixture model to obtain second result information; and alarming is carried out according to the first result information or the second result information, so that the mechanical fault of the wind driven generator can be detected in time, and the detection accuracy is improved.
Example three:
fig. 9 is a schematic view of an early warning system of a wind turbine provided by a third embodiment of the present invention.
Referring to fig. 9, the system includes: the wind driven generator comprises a data acquisition unit, a data transmission unit and a data processing unit, wherein in the data acquisition unit, an audio time domain signal is acquired through a sound pick-up which is arranged below a front cross beam of an engine room of the wind driven generator, so that the monitoring of abnormal sound of blades and the engine room can be considered, and the transmission of the audio time domain signal is facilitated. The sound pick-up is connected with the unit router through a network, and the unit router is connected with the switch in the data processing unit through an optical fiber in the data transmission unit. In the data processing unit, the central control room comprises a switch, an audio interface, equipment management and audio analysis, wherein the audio analysis comprises decibel processing alarm and spectrum analysis alarm.
And in the process of decibel processing alarm, comparing the decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain first result information.
In the process of spectral analysis alarming, Fourier transform is carried out on the audio frequency time domain signal to obtain frequency domain characteristic data; inputting the frequency domain characteristic data into a Gaussian mixture model to obtain second result information; and alarming according to the first result information or the second result information, specifically referring to the abnormal result information schematic diagram of fig. 10.
By detecting and analyzing the audio time domain signals inside the wind driven generator, the running state of mechanical components of the wind driven generator is known, so that hidden dangers are found in time, and the safe running of the wind driven generator is guaranteed.
The embodiment of the invention further provides an electronic device, which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the processor implements the steps of the early warning method of the wind driven generator provided by the embodiment when executing the computer program.
The embodiment of the invention also provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the early warning method for the wind driven generator of the embodiment are executed.
The computer program product provided in the embodiment of the present invention includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, which is not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (8)
1. An early warning method for a wind turbine, the method comprising:
collecting an audio time domain signal;
comparing the decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain first result information;
performing Fourier transform on the audio time domain signal to obtain frequency domain characteristic data;
inputting the frequency domain characteristic data into a Gaussian mixture model to obtain second result information;
alarming according to the first result information or the second result information;
and calling an abnormal database by the Gaussian mixture model, comparing the frequency domain characteristic data with the frequency domain characteristic data in the abnormal database, and generating abnormal result information if the similarity between the frequency domain characteristic data and the frequency domain characteristic data in the abnormal database reaches a matching threshold value.
2. The early warning method for the wind driven generator according to claim 1, wherein the fourier transforming the audio time domain signal to obtain frequency domain feature data comprises:
framing the audio time domain signal to obtain a multi-frame audio time domain signal;
and respectively carrying out Fourier transform on each frame of audio time domain signal to obtain the frequency domain characteristic data.
3. The early warning method for wind driven generators according to claim 1, wherein the comparing the decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain a first result information includes:
judging whether a decibel value corresponding to the audio time domain signal is greater than the decibel threshold value or not;
and if the decibel value corresponding to the audio time domain signal is greater than the decibel threshold value, generating alarm information.
4. The early warning method for wind driven generator according to claim 3, wherein the determining whether the decibel value corresponding to the audio time domain signal is greater than the decibel threshold value comprises:
and if the decibel value corresponding to the audio time domain signal is smaller than the decibel threshold value, generating normal result information.
5. An early warning device for a wind turbine, the device comprising:
the acquisition unit is used for acquiring audio time domain signals;
the comparison unit is used for comparing the decibel value corresponding to the audio time domain signal with a decibel threshold value to obtain first result information;
the frequency domain characteristic data acquisition unit is used for carrying out Fourier transform on the audio time domain signal to obtain frequency domain characteristic data;
the second result information acquisition unit is used for inputting the frequency domain characteristic data into a Gaussian mixture model to obtain second result information;
the alarm unit is used for giving an alarm according to the first result information or the second result information;
and calling an abnormal database by the Gaussian mixture model, comparing the frequency domain characteristic data with the frequency domain characteristic data in the abnormal database, and generating abnormal result information if the similarity between the frequency domain characteristic data and the frequency domain characteristic data in the abnormal database reaches a matching threshold value.
6. The wind turbine early warning device according to claim 5, wherein the frequency domain feature data obtaining unit comprises:
framing the audio time domain signal to obtain a multi-frame audio time domain signal;
and respectively carrying out Fourier transform on each frame of audio time domain signal to obtain the frequency domain characteristic data.
7. The wind turbine early warning device according to claim 5, wherein the comparison unit comprises:
judging whether a decibel value corresponding to the audio time domain signal is greater than the decibel threshold value or not;
and if the decibel value corresponding to the audio time domain signal is greater than the decibel threshold value, generating alarm information.
8. The wind turbine early warning device according to claim 7, wherein the comparison unit comprises:
and if the decibel value corresponding to the audio time domain signal is smaller than the decibel threshold value, generating normal result information.
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| CN101393049A (en) * | 2008-08-25 | 2009-03-25 | 北京天源科创风电技术有限责任公司 | Vibration monitoring and failure diagnosis method for wind generating set |
| CN102819018A (en) * | 2011-06-10 | 2012-12-12 | 索尼公司 | Signal processing unit and method |
| CN103810374A (en) * | 2013-12-09 | 2014-05-21 | 中国矿业大学 | Machine fault prediction method based on MFCC feature extraction |
| CN103744021A (en) * | 2013-12-23 | 2014-04-23 | 煤炭科学研究总院 | Apparatus and method for motor fault monitoring |
| CN105136435A (en) * | 2015-07-15 | 2015-12-09 | 北京汉能华科技股份有限公司 | Wind generating set blade fault diagnosis method and device |
| CN105352541A (en) * | 2015-10-14 | 2016-02-24 | 国网福建省电力有限公司 | Transformer operation auxiliary monitoring system and method based on disaster prevention and reduction system of power grid |
| CN106568500A (en) * | 2016-10-12 | 2017-04-19 | 上海大学 | Processing abnormality alarm system based on processing noise |
| CN106593781A (en) * | 2016-11-29 | 2017-04-26 | 上海电机学院 | Wind driven generator fault detecting system and method based on Android platform |
| CN107061186A (en) * | 2017-06-09 | 2017-08-18 | 北京金风慧能技术有限公司 | Vibration of wind generating set abnormity early warning method and apparatus |
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